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# packages required
# install.packages("gridExtra")
#packages = c("gtrendsR","tidyverse","usmap")
#install.packages("mapproj")
# importing libraries
library(dplyr)
library(tidyverse)
library(data.table)
library(ggplot2)
library(scales)
library(gridExtra)
# read in NYtimes Covid data tables
# Cumulative Daily Cases and Deaths by
# 1 - County Level
cty_data <-read.csv("us-counties.csv")
# 2 - State Level
state_data <-read.csv("us-states.csv")
state_pop <-read.csv("state_pop.csv")
# 3 -
us_data <-read.csv("us.csv")
# transforming date column from CHARACTER to DATE class
cty_data$date <- as.Date(cty_data$date)
state_data$date <- as.Date(state_data$date,format="%Y-%m-%d")
us_data$date <- as.Date(us_data$date)
# filter out dates before March 1st, 2020
cty_data <- base::subset(cty_data,date>"2020-02-29")
state_data <- base::subset(state_data,date>"2020-02-29")
us_data <- base::subset(us_data,date>"2020-02-29")
# Examination of US-wide Temporal Trends
# Use `filter()` to add a column named `new_cases` and 'new_deaths'
us_data$new_cases = as.numeric(stats::filter(us_data$cases,filter=c(1,-1), sides=1))
us_data$new_deaths = as.numeric(stats::filter(us_data$deaths,filter=c(1,-1), sides=1))
# Change first entry fo new deaths and new cases from NA to 0
us_data$new_cases[1]<-0
us_data$new_deaths[1]<-0
# Add Data on Season
season <- c("Spring","Summer","Winter","Fall")
# create df for seasons
season_df = data.frame(c(1:12),c("Winter","Winter","Spring","Spring","Spring","Summer","Summer","Summer","Fall","Fall","Fall","Winter"))
names(season_df)[1] <- "month"
names(season_df)[2] <- "season"
for (i in 1:length(us_data$date))
{
if (month(us_data$date[i]) >= 3 & month(us_data$date[i]) < 6) {
us_data$season[i] = season[1]
} else if (month(us_data$date[i]) >= 6 & month(us_data$date[i]) < 9) {
us_data$season[i] = season[2]
} else if (month(us_data$date[i]) >= 9 & month(us_data$date[i]) < 12) {
us_data$season[i] = season[3]
} else {
us_data$season[i] = season[4]
}
}
us_data$season = as.factor(us_data$season)
## Plotting Below
# Timeseries of Covid Cases and Deaths through time
# Value used to transform the data
p1 <- ggplot(us_data, aes(x = date, y = new_cases)) +
geom_line(color = "darkred") +
labs(title="US Covid-19 Cases and Deaths", y="Daily New Cases", x="")
p2 <- ggplot(us_data, aes(x = date, y = new_deaths)) +
geom_line(color = "black") +
labs( y="Daily New Deaths", x="Date")
grid.arrange(p1,p2,nrow=2)

# scatter plot comparison of Cases Vs Deaths
gg <- ggplot(us_data,aes(x = new_cases, y = new_deaths, colour = season)) +
geom_point() +
labs(title="Cases Vs Deaths",subtitle="US Country-wide Covid Counts",y="Daily Deaths", x="Daily Cases")
plot(gg)








regions <- state.region
demographics <- state.x77
demographics = data.frame(demographics)
states_info <- data.frame("Name" = states_vect,"Region" = regions,"Pop"=demographics$Population)
options
function (...)
.Internal(options(...))
<bytecode: 0x7ff0e39b9a78>
<environment: namespace:base>
#par(mfrow=c(2,1))
for (val in states_vect)
{
# subset by state
dum <- subset(state_data3, state == val)
if (val == "Alabama"){
plot(dum$date,dum$pc_new_cases,"l", xlab="Date", ylab="Dly Per Capita Cases",ylim=c(0,0.25e-2),cex=3/4)
par(new = TRUE)
plot(dum$date,dum$pc_new_deaths,"l", xlab="", ylab="",axes = FALSE,col=rgb(red = 1, green = 0, blue = 0, alpha = 0.5),ylim=c(0,0.1e-3))
axis(side=4, col.axis = "dark red",col.ticks = "dark red", ylim=c(0,0.1e-3))
mtext("Daily \n Per Capita Deaths",side=4,line=3, col = "dark red",cex=2/3)
title(val)
}else{
plot(dum$date,dum$pc_new_cases,"l", xlab="", ylab="",ylim=c(0,0.25e-2))
par(new = TRUE)
plot(dum$date,dum$pc_new_deaths,"l", xlab="", ylab="",axes = FALSE,col=rgb(red = 1, green = 0, blue = 0, alpha = 0.5), ylim=c(0,0.1e-3))
axis(side=4, col.axis = "dark red",col.ticks = "dark red")
title(val)}
}


















































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---
title: "STA 220 - Final Project Data Analaysis"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

```{r}
# packages required
# install.packages("gridExtra")
#packages = c("gtrendsR","tidyverse","usmap")
#install.packages("mapproj")
# importing libraries
library(dplyr)
library(tidyverse)
library(data.table)
library(ggplot2)  
library(scales)
library(gridExtra)

```

```{r}
# read in NYtimes Covid data tables 

# Cumulative Daily Cases and Deaths by 
# 1 - County Level
cty_data <-read.csv("us-counties.csv") 

# 2 - State Level
state_data <-read.csv("us-states.csv")
state_pop <-read.csv("state_pop.csv")

# 3 - 
us_data <-read.csv("us.csv")
```

```{r}
# transforming date column from CHARACTER to DATE class
cty_data$date <- as.Date(cty_data$date)
state_data$date <- as.Date(state_data$date,format="%Y-%m-%d")
us_data$date <- as.Date(us_data$date)
```

```{r}
# filter out dates before March 1st, 2020
cty_data <- base::subset(cty_data,date>"2020-02-29")
state_data <- base::subset(state_data,date>"2020-02-29")
us_data <- base::subset(us_data,date>"2020-02-29")
```

```{r}
# Examination of US-wide Temporal Trends

# Use `filter()` to add a column named `new_cases` and 'new_deaths'
us_data$new_cases = as.numeric(stats::filter(us_data$cases,filter=c(1,-1), sides=1))

us_data$new_deaths = as.numeric(stats::filter(us_data$deaths,filter=c(1,-1), sides=1))
 
# Change first entry fo new deaths and new cases from NA to 0
us_data$new_cases[1]<-0
us_data$new_deaths[1]<-0

# Add Data on Season
season <- c("Spring","Summer","Winter","Fall")

# create df for seasons
season_df = data.frame(c(1:12),c("Winter","Winter","Spring","Spring","Spring","Summer","Summer","Summer","Fall","Fall","Fall","Winter"))
names(season_df)[1] <- "month"
names(season_df)[2] <- "season"

for (i in 1:length(us_data$date))
{
  if (month(us_data$date[i]) >= 3 & month(us_data$date[i]) < 6) {
    us_data$season[i] = season[1]
  } else if (month(us_data$date[i]) >= 6 & month(us_data$date[i]) < 9) {
    us_data$season[i] = season[2]
  } else if (month(us_data$date[i]) >= 9 & month(us_data$date[i]) < 12) {
    us_data$season[i] = season[3]
  } else {
    us_data$season[i] = season[4]
  }
}
us_data$season = as.factor(us_data$season)

## Plotting Below

# Timeseries of Covid Cases and Deaths through time
# Value used to transform the data
p1 <- ggplot(us_data, aes(x = date, y = new_cases)) +
            geom_line(color = "darkred") +
  labs(title="US Covid-19 Cases and Deaths",  y="Daily New Cases", x="")
  

p2 <- ggplot(us_data, aes(x = date, y = new_deaths)) +
            geom_line(color = "black") +
  labs(  y="Daily New Deaths", x="Date")
  
grid.arrange(p1,p2,nrow=2)

# scatter plot comparison of Cases Vs Deaths
gg <- ggplot(us_data,aes(x = new_cases, y = new_deaths, colour = season)) +
  geom_point() +
   labs(title="Cases Vs Deaths",subtitle="US Country-wide Covid Counts",y="Daily Deaths", x="Daily Cases")

plot(gg)
```

```{r, echo = FALSE}
## Examining State Spatial Trends

# 1 - Formatting Data

# remove leading character in State_Pop$State
state_pop$State <- substring(state_pop$State,2)

# remove commas from population and convert to numeric
state_pop$Population <- as.numeric(gsub(",","",state_pop$Population))


# add state population data
state_data <- merge(state_data, state_pop, by.x="state", by.y="State")


# get population density
# remove DC from state_pop list
state_pop <-state_pop [-c(9), ] 
state.density = state_pop$Population/state.area

## get state regional data
states_info <- data.frame(state.name,state.region,state.density)
names(states_info)[1] <- "Name"
names(states_info)[2] <- "Region"
names(states_info)[3] <- "Density"

# add state region data
state_data <- merge(state_data, states_info, by.x="state", by.y="Name")

# categorize into density groups

for (i in 1:length(state_data$Density))
{
  if (state_data$Density[i] <= 50) {
     state_data$Density2[i] <- "<=50"
  } else if (state_data$Density[i] >  50 & state_data$Density[i] <= 100) {
    state_data$Density2[i] <- "50-100"
  } else if (state_data$Density[i] >  100 & state_data$Density[i] <= 400) {
    state_data$Density2[i] <- "100-400"
  } else {
    state_data$Density2[i] <- ">400"
  }
}
state_data$Density2 <- factor(state_data$Density2,
    levels = c('<=50','50-100','100-400','>400'),ordered = TRUE)

# sort by date
state_data<-state_data[order((state_data$date)),]

# Find unique fips values to find unique counties
fips_vect <- unique(cty_data$fips)

# Find unique dates
dates_vect <- unique(cty_data$date)

# find unique states (Exclude territories)
states_vect <-state.name;

# Find number of daily new cases and deaths by state
i <-1

# looping through unique state names
for (val in states_vect)
{
  # subset by state
  dum <- subset(state_data, state == val)
  
  #Use `filter()` to add a column named `new_cases` 
  dum$new_cases = as.numeric(stats::filter(dum$cases,filter=c(1,-1), sides=1))

  # Set Na values to 0. These occur on the first date of obervations
  dum$new_cases[is.na(dum$new_cases)] <- 0
  
  #Use `filter()` to add a column named `new_deaths` 
  dum$new_deaths = as.numeric(stats::filter(dum$deaths,filter=c(1,-1), sides=1))
  
    # Set NaN values to 0. These occur on the first date of obervations
  dum$new_deaths[is.na(dum$new_deaths)] <- 0
  
  # append to dataframe
  if (i>1){
    df1 <- rbind(df1,dum)
  }else{
    df1 <- dum
  }
  i <- i+1
  rm(dum)
}
# final new data frame
state_data2 <- df1

#calculate per capita case and death rates
state_data2$pc_new_cases <- state_data2$new_cases / state_data2$Population
state_data2$pc_new_deaths <- state_data2$new_deaths / state_data2$Population

# extracting year and month
month <- data.frame(format(state_data2$date,format="%m/%y"))
# month <- data.frame(format(state_data2$date,format="%m"))

# add as columns to df
# state_data2<-bind_cols(state_data2,year)
state_data2<-bind_cols(state_data2,month)

# sorting columns
state_data3 <- state_data2[,c(1,14,2:13)]

# renaming columns
names(state_data3)[2] <- "month"

# Calculate pc avg cases for each state
state_mnthly_cases = as.data.table(state_data3)[, mean(pc_new_cases), by = .(month,state, Region,Density2)]
names(state_mnthly_cases)[5] <- "avg_pc_cases"
state_mnthly_cases$Density2 = as.factor(state_mnthly_cases$Density2)
names(state_mnthly_cases)[4] <- "Density"

# Calculate avg deaths for each state
state_mnthly_deaths = as.data.table(state_data3)[, mean(pc_new_deaths), by = .(month,state,Region,Density2)]
names(state_mnthly_deaths)[5] <- "avg_pc_deaths"
state_mnthly_deaths$Density2 = as.factor(state_mnthly_deaths$Density2)
names(state_mnthly_deaths)[4] <- "Density"

# Boxplot of Monthly Cumulative Cases by State

# getting dates in order
dum = unique(state_mnthly_cases$month)
state_mnthly_cases$month <- factor(state_mnthly_cases$month , levels=dum)
state_mnthly_deaths$month <- factor(state_mnthly_deaths$month , levels=dum)

# plotting below
# No coloring
mnthly_case_bplot = ggplot(state_mnthly_cases,aes(month, avg_pc_cases)) + 
geom_boxplot() 
mnthly_case_bplot + labs(y = "Mean Per Capita Daily Cases by State",x = "Month",
                   title = "Box Plot of Monthly Per Capita Covid-19 Cases by State") 

mnthly_death_bplot = ggplot(state_mnthly_deaths,aes(month, avg_pc_deaths)) + geom_boxplot() 
mnthly_death_bplot + labs(y = "Mean Per Capita Daily Deaths by State",x = "Month",
                   title = "Box Plot of Monthly Capita Covid-19 Deaths by State")  

# Region Map
all_states <- map_data("state")
all_states$region <- str_to_title(all_states$region)  
stateData <- merge(all_states,states_info,by.x="region",by.y = "Name" )

regionplot <- ggplot()+geom_polygon(data=stateData,aes(x=long, y=lat, group = group, fill=Region),color="grey50")+coord_map()
regionplot

# Color by region
mnthly_case_bplot2 = ggplot(state_mnthly_cases,aes(month, avg_pc_cases)) + 
geom_boxplot(aes(color = Region)) 
mnthly_case_bplot2 + labs(y = "Mean Per Capita Daily Cases by Region",x = "Month",
                   title = "Box Plot of Monthly Per Capita Covid-19 Cases by Region")  

mnthly_death_bplot2 = ggplot(state_mnthly_deaths,aes(month, avg_pc_deaths)) + 
geom_boxplot(aes(color = Region)) 
mnthly_death_bplot2 + labs(y = "Mean Daily Per Capita Deaths by Region",x = "Month",
                   title = "Box Plot of Monthly Per Capita Covid-19 Deaths by Region") 
# Color by density
mnthly_case_bplot3 = ggplot(state_mnthly_cases,aes(month, avg_pc_cases)) + 
geom_boxplot(aes(color = Density)) 
mnthly_case_bplot3 + labs(y = "Mean Per Capita Daily Cases by Density",x = "Month",
                   title = "Box Plot of Monthly Per Capita Covid-19 Cases by Density",fill = "Density [pop/mi^2]")  

mnthly_death_bplot3 = ggplot(state_mnthly_deaths,aes(month, avg_pc_deaths)) + 
geom_boxplot(aes(color = Density)) 
mnthly_death_bplot3 + labs(y = "Mean Daily Per Capita Deaths by Desnsity",x = "Month",
                   title = "Box Plot of Monthly Per Capita Covid-19 Deaths by Density",fill = "Density [pop/mi^2]") 
```
```{r}
regions <- state.region
demographics <- state.x77
demographics = data.frame(demographics)
states_info <- data.frame("Name" = states_vect,"Region" = regions,"Pop"=demographics$Population)

```

```{r}
options
#par(mfrow=c(2,1))
for (val in states_vect)
{
  # subset by state
  dum <- subset(state_data3, state == val)
  
    if (val == "Alabama"){
  plot(dum$date,dum$pc_new_cases,"l",  xlab="Date", ylab="Dly Per Capita Cases",ylim=c(0,0.25e-2),cex=3/4)
  par(new = TRUE)
  plot(dum$date,dum$pc_new_deaths,"l",  xlab="", ylab="",axes = FALSE,col=rgb(red = 1, green = 0, blue = 0, alpha = 0.5),ylim=c(0,0.1e-3))
  axis(side=4, col.axis = "dark red",col.ticks = "dark red", ylim=c(0,0.1e-3))
  mtext("Daily \n Per Capita Deaths",side=4,line=3, col = "dark red",cex=2/3)
  title(val)
  }else{
    
  plot(dum$date,dum$pc_new_cases,"l",  xlab="", ylab="",ylim=c(0,0.25e-2))
  par(new = TRUE)
  plot(dum$date,dum$pc_new_deaths,"l",  xlab="", ylab="",axes = FALSE,col=rgb(red = 1, green = 0, blue = 0, alpha = 0.5), ylim=c(0,0.1e-3))
  axis(side=4, col.axis = "dark red",col.ticks = "dark red")
  title(val)}
}
```
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When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

